1. Field of the Invention
The present invention relates to a machine learning apparatus and method learning predicted life of a power device, and a life prediction apparatus and motor driving apparatus including the machine learning apparatus.
2. Description of the Related Art
In a motor driving apparatus which drives AC motors within a machine tool, a forging machine, an injection molding machine, an industrial machine, or various robots, an inverter converts DC power into AC power to supply driving power for driving an AC motor. The inverter is implemented in a bridge circuit of a switching unit including a power device (solid-state-switching device) and a diode connected in inverse parallel to it, such as a PWM inverter, and converts DC power into AC power by ON/OFF driving in the power device and outputs the AC power to the AC motor side.
In a field using such a motor driving apparatus, in order to prevent a decline in operating efficiency and a serious accident, life of a power device is predicted, and a power device is replaced before the power device becomes unoperatable due to the end of the life, on the basis of the prediction result.
As described in e.g., Japanese Laid-open Patent Publication No. 2011-196703, a method is known in which entire operating temperature range of a semiconductor device configured by a power semiconductor device is divided into a plurality of temperature sections, a cycle number is computed by using a value which is weighted to a power cycle number in a set base temperature difference in each temperature section, and an accumulation damage is computed by using a minor rule based on the cycle numbers each computed, between the divided temperature sections to predict the life.
Life of a power device in an inverter in a motor driving apparatus changes depending on environmental conditions, such as operating conditions of a motor driving apparatus and ambient air temperature, and therefore it is not easy to predict the life accurately. When the life of a power device cannot be predicted accurately, the operator may miss the timing for replacement of a power device, which may result in a decline in operating efficiency and a serious accident. In addition, it may result in unnecessary replacement of a power device. Therefore, it is important to be able to predict the end of life of a power device.